Groq vs. DeepSeek vs. Llama-3.3-70B: Why I Mix Models Instead of Picking One

The question "which AI model is best?" is the wrong question. It is the equivalent of asking which tool is best — screwdriver, hammer, or wrench. The answer depends entirely on what you are building.

In my agent system, I run three models across different tasks. Here is the routing logic, the reasoning behind it, and the actual cost numbers.

The Models in Use

Llama-3.3-70B via Groq — Groq is an inference provider, not a model creator. They run Llama models on custom hardware (LPUs) that delivers significantly faster inference than GPU-based providers. A 1,500-word content draft completes in 6–10 seconds on Groq versus 45–90 seconds on a GPU provider.

DeepSeek R1 — DeepSeek's reasoning model. Trained with reinforcement learning to show its chain-of-thought before answering. Strong on multi-step analysis, weaker on creative writing. Pricing is significantly lower than comparable models from OpenAI or Anthropic.

Anthropic Claude (tool-use runs) — Used specifically for the agentic tool-use loop, where the model needs to make reliable function calls, interpret the results, and decide whether to make another call or return to the user. Tool use reliability on other models is inconsistent enough that I do not use them for this purpose.

The Routing Logic

Task type → Model selection:

Long-form content draft → Llama-3.3-70B via Groq
  Why: Fast inference matters for UX. Content quality is strong with a good system prompt.
  Speed: 6–10 seconds for 1,500 words.

Multi-step reasoning / analysis → DeepSeek R1
  Why: Chain-of-thought reasoning is stronger. Price-to-quality is best in class for analysis tasks.
  Example: "Review the client pipeline and identify which prospects are most likely to close this month."

Tool-use agent loop → Anthropic Claude
  Why: Function calling reliability is highest. The loop needs to correctly call tools in sequence,
  parse results, and decide next steps. This is not the place to optimize for cost.

The Actual Cost Numbers

Running three agents daily on weekdays, the monthly AI spend is approximately $0.0035.

That number is real, and it reflects two things: the agents run on small context windows with efficient prompts, and Groq's pricing for Llama is extremely low at this volume.

For reference, the cost breakdown is roughly:

• —Groq (Llama-3.3-70B): ~$0.59 per million input tokens, ~$0.79 per million output tokens
• —DeepSeek R1: ~$0.14 per million input tokens, ~$2.19 per million output tokens (varies)
• —Anthropic: higher per token, but tool-use runs are short

At the volume I run (weekday-only, focused prompts, short outputs), the total is trivially small.

What Each Model Gets Wrong

Llama via Groq — Occasionally loses instruction-following on very long or very specific formatting instructions. If your system prompt has 10 constraints, it might respect 8. For content generation where the output is edited anyway, this is acceptable. For structured data extraction, it is not.

DeepSeek R1 — The chain-of-thought is visible in the API response (as a separate field). This is useful for debugging but means the output is larger. It also occasionally over-thinks simple tasks — writing three paragraphs of reasoning before concluding something obvious.

Anthropic — The most expensive per token. For tasks where cost matters at scale, using Claude for everything is not efficient. For the agentic loop specifically, the reliability justifies the cost.

When to Use One Model

If you are just starting and need one model for all tasks: Claude Haiku for most tasks, Claude Sonnet for reasoning tasks. Simple, reliable, good quality. More expensive than mixing, but significantly less complexity.

The multi-model approach I use makes sense once you have a clear enough picture of your task types to route them correctly. Before that point, the overhead of managing multiple API keys and debugging model-specific issues is not worth it.

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Resources

• —Groq console and pricing — sign up, get an API key, and see the per-token pricing for Llama-3.3-70B and other hosted models
• —DeepSeek API platform — pricing, rate limits, and the reasoning model (R1) documentation
• —Anthropic API pricing — token pricing for Claude Haiku, Sonnet, and Opus; compare against Groq and DeepSeek for your task types
• —OpenRouter — a unified API that routes to 100+ models; useful for testing model switching before building dedicated provider integrations

Related Posts

• —I Built My Own AI Content Team: Content Manager, CMO, and CTO Agents — the agent system that runs on this multi-model routing setup
• —API Key Management: Building a Central Vault for Multiple AI Providers — how to manage keys for all three providers from one admin panel
• —My Claude Code and Antigravity Workflow for Running 3 Projects at Once — how Claude Code itself fits into the AI tooling stack

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